This paper presents Response Surface Methodology (RSM) modeling techniques to solve the engine mount optimization problem for motorcycle applications. A theoretical model that represents the structural dynamics of the engine mount system in motorcycles is first used to build the RSM model. The RSM model is then used to solve the engine mount optimization problem to enhance vibration isolation. This leads to a substantial reduction in computational effort and simplifies the governing model, yielding an input-output relationship between the variables of interest. Design of Experiments (DOE) techniques are used to build the RSM model from the theoretical model. Full factorial and fractional factorial formulations are used to construct the governing experiments. Normal probability plots are used to determine the statistical significance of the resulting coefficients. The statistically significant variables are then used to build the response surface. The design variables for the engine mount optimization problem include mount stiffness, position and orientation vectors. The influence of the orientation variables is highly non-linear and is difficult to model by using a response surface consisting of lower order terms only. Two separate algorithms are proposed to overcome this problem and the results from the RSM models are compared to those from the theoretical model.

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